Human papillomavirus (HPV) infection is the primary cause of cervical, anogenital, and oropharyngeal cancers in the United States. These cancers are preventable through HPV vaccination. Research is critically needed to identify effective strategies for promoting HPV vaccination among high-risk groups. This study develops a risk prediction model to identify patients who are unlikely to complete HPV vaccination, with the goal of using the model to direct resources and increase vaccination rates.
We assessed vaccination status along with patient, provider, and clinic characteristics that predict vaccination completion. We then developed a predictive model to assess the likelihood of completing HPV vaccination, which can be used to target interventions based on patient needs. We used a retrospective cohort from a large integrated delivery system in Oregon. Using logistic regression with data available in the electronic health record, we created a risk model to determine the likelihood of vaccination completion among patients aged 11–17 years.
In a cohort of 61,788 patients, 40,570 (65.7%) had received at least one dose of the HPV vaccine. The full model included 17 demographic, clinical, provider, and community characteristics, achieving a bootstrap-corrected C-statistic of 0.67 with adequate calibration. The reduced model, which retained five demographic and clinical characteristics (age, language, race, ethnicity, and prior vaccinations), had a bootstrap-corrected C-statistic of 0.65 and adequate calibration.
Our findings suggest that a risk prediction model can guide the implementation of targeted interventions and the intensity of those interventions based on the likelihood of vaccination completion.
Full text
https://www.xiahepublishing.com/2835-3315/CSP-2024-00026
The study was recently published in the Cancer Screening and Prevention .
Cancer Screening and Prevention (CSP) publishes high-quality research and review articles related to cancer screening and prevention. It aims to provide a platform for studies that develop innovative and creative strategies and precise models for screening, early detection, and prevention of various cancers. Studies on the integration of precision cancer prevention multiomics where cancer screening, early detection and prevention regimens can precisely reflect the risk of cancer from dissected genomic and environmental parameters are particularly welcome.
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Cancer Screening and Prevention
The Development of a Risk Prediction Model to Predict Patients’ Likelihood of Completing Human Papillomavirus Vaccination
25-Dec-2024